{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN for Human Activity Recognition - 2D Pose Input\n",
    "\n",
    "This experiment is the classification of human activities using a 2D pose time series dataset and an LSTM RNN.\n",
    "The idea is to prove the concept that using a series of 2D poses, rather than 3D poses or a raw 2D images, can produce an accurate estimation of the behaviour of a person or animal.\n",
    "This is a step towards creating a method of classifying an animal's current behaviour state and predicting it's likely next state, allowing for better interaction with an autonomous mobile robot.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "The aims of this experiment are:\n",
    "\n",
    "-  To determine if 2D pose has comparable accuracy to 3D pose for use in activity recognition. This would allow the use of RGB only cameras for human and animal pose estimation, as opposed to RGBD or a large motion capture dataset.\n",
    "\n",
    "\n",
    "- To determine if  2D pose has comparable accuracy to using raw RGB images for use in activity recognition. This is based on the idea that limiting the input feature vector can help to deal with a limited dataset, as is likely to occur in animal activity recognition, by allowing for a smaller model to be used (citation required).\n",
    "\n",
    "\n",
    "- To verify the concept for use in future works involving behaviour prediction from motion in 2D images.\n",
    "\n",
    "The network used in this experiment is based on that of Guillaume Chevalier, 'LSTMs for Human Activity Recognition, 2016'  https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition, available under the MIT License.\n",
    "Notable changes that have been made (other than accounting for dataset sizes) are:\n",
    " - Adapting for use with a large dataset ordered by class, using random sampling without replacement for mini-batch.  \n",
    " This allows for use of smaller batch sizes when using a dataset ordered by class. \"It has been observed in practice that when using a larger batch there is a significant degradation in the quality of the model, as measured by its ability to generalize\"  \n",
    "      _N.S Keskar, D. Mudigere, et al, 'On Large-Batch Training for Deep Learning: Generalization Gap and Sharp \n",
    "      Minima', ICLR 2017_ https://arxiv.org/abs/1609.04836\n",
    "      \n",
    " - Exponentially decaying learning rate implemented\n",
    "\n",
    "\n",
    "\n",
    "## Dataset overview\n",
    "\n",
    "The dataset consists of pose estimations, made using the software OpenPose (https://github.com/CMU-Perceptual-Computing-Lab/openpose's) on a subset of the Berkeley Multimodal Human Action Database (MHAD) dataset http://tele-immersion.citris-uc.org/berkeley_mhad.\n",
    "\n",
    "This dataset is comprised of 12 subjects doing the following 6 actions for 5 repetitions, filmed from 4 angles, repeated 5 times each.  \n",
    "\n",
    "- JUMPING,\n",
    "- JUMPING_JACKS,\n",
    "- BOXING,\n",
    "- WAVING_2HANDS,\n",
    "- WAVING_1HAND,\n",
    "- CLAPPING_HANDS.\n",
    "\n",
    "In total, there are 1438 videos (2 were missing) made up of 211200 individual frames.\n",
    "\n",
    "The below image is an example of the 4 camera views during the 'boxing' action for subject 1\n",
    "\n",
    "![alt text](images/boxing_all_views.gif.png \"Title\")\n",
    "\n",
    "The input for the LSTM is the 2D position of 18 joints across a timeseries of frames numbering n_steps (window-width), with an associated class label for the frame series.  \n",
    "A single frame's input (where j refers to a joint) is stored as:\n",
    "\n",
    "[  j0_x,  j0_y, j1_x, j1_y , j2_x, j2_y, j3_x, j3_y, j4_x, j4_y, j5_x, j5_y, j6_x, j6_y, j7_x, j7_y, j8_x, j8_y, j9_x, j9_y, j10_x, j10_y, j11_x, j11_y, j12_x, j12_y, j13_x, j13_y, j14_x, j14_y, j15_x, j15_y, j16_x, j16_y, j17_x, j17_y ]\n",
    "\n",
    "For the following experiment, very little preprocessing has been done to the dataset.  \n",
    "The following steps were taken:\n",
    "1. openpose run on individual frames, for each subject, action and view, outputting JSON of 18 joint x and y position keypoints and accuracies per frame\n",
    "2. JSONs converted into txt format, keeping only x and y positions of each frame, action being performed during frame, and order of frames. This is used to create a database of associated activity class number and corresponding series of joint 2D positions\n",
    "3. No further prepossessing was performed.  \n",
    "\n",
    "In some cases, multiple people were detected in each frame, in which only the first detection was used.\n",
    "\n",
    "The data has not been normalised with regards to subject position in the frame, motion across frame (if any), size of the subject, speed of action etc. It is essentially the raw 2D position of each joint viewed from a stationary camera.  \n",
    "In many cases, individual joints were not located and a position of [0.0,0.0] was given for that joint\n",
    "\n",
    "A summary of the dataset used for input is:\n",
    "\n",
    " - 211200 individual images \n",
    " - n_steps = 32 frames (~=1.5s at 22Hz)\n",
    " - Images with noisy pose detection (detection of >=2 people) = 5132  \n",
    " - Training_split = 0.8\n",
    " - Overlap = 0.8125 (26 / 32) ie 26 frame overlap\n",
    "   - Length X_train = 22625 * 32 frames\n",
    "   - Length X_test = 5751 * 32 frames\n",
    "   \n",
    "Note that their is no overlap between test and train sets, which were seperated by activity repetition entirely, before creating the 26 of 32 frame overlap.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Training and Results below: \n",
    "Training took approximately 4 mins running on a single GTX1080Ti, and was run for 22,000,000ish iterations with a batch size of 5000  (600 epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf  # Version 1.0.0 (some previous versions are used in past commits)\n",
    "from sklearn import metrics\n",
    "import random\n",
    "from random import randint\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Useful Constants\n",
    "\n",
    "# Output classes to learn how to classify\n",
    "LABELS = [    \n",
    "    \"JUMPING\",\n",
    "    \"JUMPING_JACKS\",\n",
    "    \"BOXING\",\n",
    "    \"WAVING_2HANDS\",\n",
    "    \"WAVING_1HAND\",\n",
    "    \"CLAPPING_HANDS\"\n",
    "\n",
    "] \n",
    "DATASET_PATH = \"data/HAR_pose_activities/database/\"\n",
    "\n",
    "X_train_path = DATASET_PATH + \"X_train.txt\"\n",
    "X_test_path = DATASET_PATH + \"X_test.txt\"\n",
    "\n",
    "y_train_path = DATASET_PATH + \"Y_train.txt\"\n",
    "y_test_path = DATASET_PATH + \"Y_test.txt\"\n",
    "\n",
    "n_steps = 32 # 32 timesteps per series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Load the networks inputs\n",
    "\n",
    "def load_X(X_path):\n",
    "    file = open(X_path, 'r')\n",
    "    X_ = np.array(\n",
    "        [elem for elem in [\n",
    "            row.split(',') for row in file\n",
    "        ]], \n",
    "        dtype=np.float32\n",
    "    )\n",
    "    file.close()\n",
    "    blocks = int(len(X_) / n_steps)\n",
    "    \n",
    "    X_ = np.array(np.split(X_,blocks))\n",
    "\n",
    "    return X_ \n",
    "\n",
    "# Load the networks outputs\n",
    "\n",
    "def load_y(y_path):\n",
    "    file = open(y_path, 'r')\n",
    "    y_ = np.array(\n",
    "        [elem for elem in [\n",
    "            row.replace('  ', ' ').strip().split(' ') for row in file\n",
    "        ]], \n",
    "        dtype=np.int32\n",
    "    )\n",
    "    file.close()\n",
    "    \n",
    "    # for 0-based indexing \n",
    "    return y_ - 1\n",
    "\n",
    "X_train = load_X(X_train_path)\n",
    "X_test = load_X(X_test_path)\n",
    "#print X_test\n",
    "\n",
    "y_train = load_y(y_train_path)\n",
    "y_test = load_y(y_test_path)\n",
    "# proof that it actually works for the skeptical: replace labelled classes with random classes to train on\n",
    "#for i in range(len(y_train)):\n",
    "#    y_train[i] = randint(0, 5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Parameters:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(X shape, y shape, every X's mean, every X's standard deviation)\n",
      "((22625, 32, 36), (5751, 1), 251.01117, 126.12204)\n",
      "\n",
      "The dataset has not been preprocessed, is not normalised etc\n"
     ]
    }
   ],
   "source": [
    "# Input Data \n",
    "\n",
    "training_data_count = len(X_train)  # 4519 training series (with 50% overlap between each serie)\n",
    "test_data_count = len(X_test)  # 1197 test series\n",
    "n_input = len(X_train[0][0])  # num input parameters per timestep\n",
    "\n",
    "n_hidden = 34 # Hidden layer num of features\n",
    "n_classes = 6 \n",
    "\n",
    "#updated for learning-rate decay\n",
    "# calculated as: decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)\n",
    "decaying_learning_rate = True\n",
    "learning_rate = 0.0025 #used if decaying_learning_rate set to False\n",
    "init_learning_rate = 0.005\n",
    "decay_rate = 0.96 #the base of the exponential in the decay\n",
    "decay_steps = 100000 #used in decay every 60000 steps with a base of 0.96\n",
    "\n",
    "global_step = tf.Variable(0, trainable=False)\n",
    "lambda_loss_amount = 0.0015\n",
    "\n",
    "training_iters = training_data_count *300  # Loop 300 times on the dataset, ie 300 epochs\n",
    "batch_size = 512\n",
    "display_iter = batch_size*8  # To show test set accuracy during training\n",
    "\n",
    "print(\"(X shape, y shape, every X's mean, every X's standard deviation)\")\n",
    "print(X_train.shape, y_test.shape, np.mean(X_test), np.std(X_test))\n",
    "print(\"\\nThe dataset has not been preprocessed, is not normalised etc\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions for training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM_RNN(_X, _weights, _biases):\n",
    "    # model architecture based on \"guillaume-chevalier\" and \"aymericdamien\" under the MIT license.\n",
    "\n",
    "    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size\n",
    "    _X = tf.reshape(_X, [-1, n_input])   \n",
    "    # Rectifies Linear Unit activation function used\n",
    "    _X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])\n",
    "    # Split data because rnn cell needs a list of inputs for the RNN inner loop\n",
    "    _X = tf.split(_X, n_steps, 0) \n",
    "\n",
    "    # Define two stacked LSTM cells (two recurrent layers deep) with tensorflow\n",
    "    lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "    lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "    lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)\n",
    "    outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)\n",
    "\n",
    "    # A single output is produced, in style of \"many to one\" classifier, refer to http://karpathy.github.io/2015/05/21/rnn-effectiveness/ for details\n",
    "    lstm_last_output = outputs[-1]\n",
    "    \n",
    "    # Linear activation\n",
    "    return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']\n",
    "\n",
    "\n",
    "def extract_batch_size(_train, _labels, _unsampled, batch_size):\n",
    "    # Fetch a \"batch_size\" amount of data and labels from \"(X|y)_train\" data. \n",
    "    # Elements of each batch are chosen randomly, without replacement, from X_train with corresponding label from Y_train\n",
    "    # unsampled_indices keeps track of sampled data ensuring non-replacement. Resets when remaining datapoints < batch_size    \n",
    "    \n",
    "    shape = list(_train.shape)\n",
    "    shape[0] = batch_size\n",
    "    batch_s = np.empty(shape)\n",
    "    batch_labels = np.empty((batch_size,1)) \n",
    "\n",
    "    for i in range(batch_size):\n",
    "        # Loop index\n",
    "        # index = random sample from _unsampled (indices)\n",
    "        index = random.choice(_unsampled)\n",
    "        batch_s[i] = _train[index] \n",
    "        batch_labels[i] = _labels[index]\n",
    "        _unsampled.remove(index)\n",
    "\n",
    "\n",
    "    return batch_s, batch_labels, _unsampled\n",
    "\n",
    "\n",
    "def one_hot(y_):\n",
    "    # One hot encoding of the network outputs\n",
    "    # e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]\n",
    "    \n",
    "    y_ = y_.reshape(len(y_))\n",
    "    n_values = int(np.max(y_)) + 1\n",
    "    return np.eye(n_values)[np.array(y_, dtype=np.int32)]  # Returns FLOATS\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Graph input/output\n",
    "x = tf.placeholder(tf.float32, [None, n_steps, n_input])\n",
    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
    "\n",
    "# Graph weights\n",
    "weights = {\n",
    "    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))\n",
    "}\n",
    "biases = {\n",
    "    'hidden': tf.Variable(tf.random_normal([n_hidden])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "\n",
    "pred = LSTM_RNN(x, weights, biases)\n",
    "\n",
    "# Loss, optimizer and evaluation\n",
    "l2 = lambda_loss_amount * sum(\n",
    "    tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()\n",
    ") # L2 loss prevents this overkill neural network to overfit the data\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss\n",
    "if decaying_learning_rate:\n",
    "    learning_rate = tf.train.exponential_decay(init_learning_rate, global_step*batch_size, decay_steps, decay_rate, staircase=True)\n",
    "\n",
    "\n",
    "#decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps) #exponentially decayed learning rate\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost,global_step=global_step) # Adam Optimizer\n",
    "\n",
    "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #4096:  Learning rate = 0.005000:   Batch Loss = 3.159604, Accuracy = 0.23046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.10966157913, Accuracy = 0.22830812633\n",
      "Iter #32768:  Learning rate = 0.005000:   Batch Loss = 2.600245, Accuracy = 0.216064453125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.55431318283, Accuracy = 0.22900365293\n",
      "Iter #65536:  Learning rate = 0.005000:   Batch Loss = 2.334033, Accuracy = 0.231201171875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.32334899902, Accuracy = 0.232829079032\n",
      "Iter #98304:  Learning rate = 0.005000:   Batch Loss = 2.264152, Accuracy = 0.27685546875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.23899412155, Accuracy = 0.293861925602\n",
      "Iter #131072:  Learning rate = 0.004800:   Batch Loss = 2.212648, Accuracy = 0.287109375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.19271469116, Accuracy = 0.280472964048\n",
      "Iter #163840:  Learning rate = 0.004800:   Batch Loss = 2.148590, Accuracy = 0.30224609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.14848566055, Accuracy = 0.304121017456\n",
      "Iter #196608:  Learning rate = 0.004800:   Batch Loss = 2.105950, Accuracy = 0.2900390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.12235975266, Accuracy = 0.28603720665\n",
      "Iter #229376:  Learning rate = 0.004608:   Batch Loss = 2.053769, Accuracy = 0.330322265625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.05479478836, Accuracy = 0.308641970158\n",
      "Iter #262144:  Learning rate = 0.004608:   Batch Loss = 1.997032, Accuracy = 0.337646484375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.04714989662, Accuracy = 0.293688058853\n",
      "Iter #294912:  Learning rate = 0.004608:   Batch Loss = 1.967660, Accuracy = 0.364990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.99953794479, Accuracy = 0.337332636118\n",
      "Iter #327680:  Learning rate = 0.004424:   Batch Loss = 1.910298, Accuracy = 0.3828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.92454147339, Accuracy = 0.354894787073\n",
      "Iter #360448:  Learning rate = 0.004424:   Batch Loss = 1.829972, Accuracy = 0.403076171875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.82967329025, Accuracy = 0.387237012386\n",
      "Iter #393216:  Learning rate = 0.004424:   Batch Loss = 1.844098, Accuracy = 0.38134765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.79486227036, Accuracy = 0.390019118786\n",
      "Iter #425984:  Learning rate = 0.004247:   Batch Loss = 1.856211, Accuracy = 0.38623046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.76797473431, Accuracy = 0.412450015545\n",
      "Iter #458752:  Learning rate = 0.004247:   Batch Loss = 1.754778, Accuracy = 0.41650390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.72434902191, Accuracy = 0.398713260889\n",
      "Iter #491520:  Learning rate = 0.004247:   Batch Loss = 1.685609, Accuracy = 0.4296875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.70348834991, Accuracy = 0.40166926384\n",
      "Iter #524288:  Learning rate = 0.004077:   Batch Loss = 1.678535, Accuracy = 0.432373046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.67185354233, Accuracy = 0.440445125103\n",
      "Iter #557056:  Learning rate = 0.004077:   Batch Loss = 1.650188, Accuracy = 0.432373046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.64736294746, Accuracy = 0.438358545303\n",
      "Iter #589824:  Learning rate = 0.004077:   Batch Loss = 1.636925, Accuracy = 0.434326171875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.65965843201, Accuracy = 0.430881589651\n",
      "Iter #622592:  Learning rate = 0.003914:   Batch Loss = 1.625807, Accuracy = 0.436279296875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.62137389183, Accuracy = 0.424621790648\n",
      "Iter #655360:  Learning rate = 0.003914:   Batch Loss = 1.613935, Accuracy = 0.431640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.80041718483, Accuracy = 0.377151787281\n",
      "Iter #688128:  Learning rate = 0.003914:   Batch Loss = 1.605900, Accuracy = 0.428955078125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.63276195526, Accuracy = 0.432446539402\n",
      "Iter #720896:  Learning rate = 0.003757:   Batch Loss = 1.601465, Accuracy = 0.43212890625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.60543632507, Accuracy = 0.437141358852\n",
      "Iter #753664:  Learning rate = 0.003757:   Batch Loss = 1.592919, Accuracy = 0.44091796875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.59177517891, Accuracy = 0.447052687407\n",
      "Iter #786432:  Learning rate = 0.003757:   Batch Loss = 1.566908, Accuracy = 0.454345703125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.57933938503, Accuracy = 0.446183264256\n",
      "Iter #819200:  Learning rate = 0.003607:   Batch Loss = 1.562364, Accuracy = 0.447509765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.57102179527, Accuracy = 0.442531734705\n",
      "Iter #851968:  Learning rate = 0.003607:   Batch Loss = 1.580286, Accuracy = 0.455078125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.57091975212, Accuracy = 0.46044164896\n",
      "Iter #884736:  Learning rate = 0.003607:   Batch Loss = 1.559277, Accuracy = 0.456298828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.57228589058, Accuracy = 0.443922787905\n",
      "Iter #917504:  Learning rate = 0.003463:   Batch Loss = 1.553041, Accuracy = 0.457763671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5565084219, Accuracy = 0.431229352951\n",
      "Iter #950272:  Learning rate = 0.003463:   Batch Loss = 1.563610, Accuracy = 0.456298828125\n",
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      "Iter #983040:  Learning rate = 0.003463:   Batch Loss = 1.531766, Accuracy = 0.453125\n",
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      "Iter #1015808:  Learning rate = 0.003324:   Batch Loss = 1.508063, Accuracy = 0.4619140625\n",
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      "Iter #1048576:  Learning rate = 0.003324:   Batch Loss = 1.691581, Accuracy = 0.400390625\n",
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      "Iter #1081344:  Learning rate = 0.003324:   Batch Loss = 1.599661, Accuracy = 0.4130859375\n",
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      "Iter #1114112:  Learning rate = 0.003191:   Batch Loss = 1.541436, Accuracy = 0.428955078125\n",
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      "Iter #1146880:  Learning rate = 0.003191:   Batch Loss = 1.522115, Accuracy = 0.45068359375\n",
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      "Iter #1179648:  Learning rate = 0.003191:   Batch Loss = 1.506870, Accuracy = 0.430908203125\n",
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      "Iter #1212416:  Learning rate = 0.003064:   Batch Loss = 1.499450, Accuracy = 0.4658203125\n",
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      "Iter #1245184:  Learning rate = 0.003064:   Batch Loss = 1.472249, Accuracy = 0.480712890625\n",
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      "Iter #1277952:  Learning rate = 0.003064:   Batch Loss = 1.443851, Accuracy = 0.4931640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.4211268425, Accuracy = 0.491566687822\n",
      "Iter #1310720:  Learning rate = 0.002941:   Batch Loss = 1.410268, Accuracy = 0.511962890625\n",
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      "Iter #1343488:  Learning rate = 0.002941:   Batch Loss = 1.397048, Accuracy = 0.482421875\n",
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      "Iter #1376256:  Learning rate = 0.002941:   Batch Loss = 1.405404, Accuracy = 0.492431640625\n",
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      "Iter #1409024:  Learning rate = 0.002823:   Batch Loss = 1.346976, Accuracy = 0.51171875\n",
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      "Iter #1441792:  Learning rate = 0.002823:   Batch Loss = 1.360385, Accuracy = 0.50390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.30642175674, Accuracy = 0.541123270988\n",
      "Iter #1474560:  Learning rate = 0.002823:   Batch Loss = 1.330049, Accuracy = 0.51025390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.27930140495, Accuracy = 0.54164493084\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #1507328:  Learning rate = 0.002710:   Batch Loss = 1.326741, Accuracy = 0.535400390625\n",
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      "Iter #1540096:  Learning rate = 0.002710:   Batch Loss = 1.304081, Accuracy = 0.5439453125\n",
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      "Iter #1572864:  Learning rate = 0.002710:   Batch Loss = 1.390152, Accuracy = 0.50634765625\n",
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      "Iter #1605632:  Learning rate = 0.002602:   Batch Loss = 1.296396, Accuracy = 0.56103515625\n",
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      "Iter #1638400:  Learning rate = 0.002602:   Batch Loss = 1.291340, Accuracy = 0.548583984375\n",
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      "Iter #1671168:  Learning rate = 0.002602:   Batch Loss = 1.293565, Accuracy = 0.554931640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.25612068176, Accuracy = 0.555729448795\n",
      "Iter #1703936:  Learning rate = 0.002498:   Batch Loss = 1.276279, Accuracy = 0.57861328125\n",
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      "Iter #1736704:  Learning rate = 0.002498:   Batch Loss = 1.286541, Accuracy = 0.5693359375\n",
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      "Iter #1769472:  Learning rate = 0.002498:   Batch Loss = 1.272370, Accuracy = 0.56298828125\n",
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      "Iter #1802240:  Learning rate = 0.002398:   Batch Loss = 1.272948, Accuracy = 0.558349609375\n",
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      "Iter #1835008:  Learning rate = 0.002398:   Batch Loss = 1.254196, Accuracy = 0.559326171875\n",
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      "Iter #1867776:  Learning rate = 0.002398:   Batch Loss = 1.253776, Accuracy = 0.56298828125\n",
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      "Iter #1900544:  Learning rate = 0.002302:   Batch Loss = 1.257756, Accuracy = 0.556884765625\n",
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      "Iter #1933312:  Learning rate = 0.002302:   Batch Loss = 1.222969, Accuracy = 0.55322265625\n",
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      "Iter #1966080:  Learning rate = 0.002302:   Batch Loss = 1.222452, Accuracy = 0.583740234375\n",
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      "Iter #1998848:  Learning rate = 0.002302:   Batch Loss = 1.224348, Accuracy = 0.58935546875\n",
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      "Iter #2031616:  Learning rate = 0.002210:   Batch Loss = 1.214390, Accuracy = 0.592041015625\n",
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      "Iter #2064384:  Learning rate = 0.002210:   Batch Loss = 1.225724, Accuracy = 0.579833984375\n",
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      "Iter #2097152:  Learning rate = 0.002210:   Batch Loss = 1.179184, Accuracy = 0.6044921875\n",
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      "Iter #2129920:  Learning rate = 0.002122:   Batch Loss = 1.145434, Accuracy = 0.612548828125\n",
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      "Iter #2162688:  Learning rate = 0.002122:   Batch Loss = 1.097031, Accuracy = 0.658447265625\n",
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      "Iter #2195456:  Learning rate = 0.002122:   Batch Loss = 1.067659, Accuracy = 0.65966796875\n",
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      "Iter #2228224:  Learning rate = 0.002037:   Batch Loss = 1.047065, Accuracy = 0.650390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.03549182415, Accuracy = 0.668579399586\n",
      "Iter #2260992:  Learning rate = 0.002037:   Batch Loss = 1.053586, Accuracy = 0.658935546875\n",
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      "Iter #2293760:  Learning rate = 0.002037:   Batch Loss = 1.046722, Accuracy = 0.6630859375\n",
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      "Iter #2326528:  Learning rate = 0.001955:   Batch Loss = 1.010601, Accuracy = 0.680908203125\n",
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      "Iter #2359296:  Learning rate = 0.001955:   Batch Loss = 0.988577, Accuracy = 0.6904296875\n",
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      "Iter #2392064:  Learning rate = 0.001955:   Batch Loss = 0.988950, Accuracy = 0.69091796875\n",
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      "Iter #2424832:  Learning rate = 0.001877:   Batch Loss = 0.989797, Accuracy = 0.697509765625\n",
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      "Iter #2457600:  Learning rate = 0.001877:   Batch Loss = 1.000112, Accuracy = 0.692138671875\n",
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      "Iter #2490368:  Learning rate = 0.001877:   Batch Loss = 1.005155, Accuracy = 0.68310546875\n",
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      "Iter #2523136:  Learning rate = 0.001802:   Batch Loss = 0.997703, Accuracy = 0.683837890625\n",
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      "Iter #2555904:  Learning rate = 0.001802:   Batch Loss = 0.983110, Accuracy = 0.691162109375\n",
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      "Iter #2588672:  Learning rate = 0.001802:   Batch Loss = 0.966061, Accuracy = 0.703857421875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.974389195442, Accuracy = 0.697965562344\n",
      "Iter #2621440:  Learning rate = 0.001730:   Batch Loss = 1.003768, Accuracy = 0.68896484375\n",
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      "Iter #2654208:  Learning rate = 0.001730:   Batch Loss = 0.981679, Accuracy = 0.695068359375\n",
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      "Iter #2686976:  Learning rate = 0.001730:   Batch Loss = 0.962772, Accuracy = 0.701171875\n",
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      "Iter #2719744:  Learning rate = 0.001661:   Batch Loss = 0.952678, Accuracy = 0.70751953125\n",
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      "Iter #2752512:  Learning rate = 0.001661:   Batch Loss = 0.966392, Accuracy = 0.700927734375\n",
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      "Iter #2785280:  Learning rate = 0.001661:   Batch Loss = 0.951601, Accuracy = 0.698486328125\n",
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      "Iter #2818048:  Learning rate = 0.001594:   Batch Loss = 0.963313, Accuracy = 0.692626953125\n",
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      "Iter #2883584:  Learning rate = 0.001594:   Batch Loss = 0.957202, Accuracy = 0.6982421875\n",
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      "Iter #2916352:  Learning rate = 0.001531:   Batch Loss = 0.954193, Accuracy = 0.70849609375\n",
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      "Iter #2949120:  Learning rate = 0.001531:   Batch Loss = 0.951909, Accuracy = 0.703369140625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #2981888:  Learning rate = 0.001531:   Batch Loss = 0.934536, Accuracy = 0.712646484375\n",
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      "Iter #3014656:  Learning rate = 0.001469:   Batch Loss = 0.946921, Accuracy = 0.70849609375\n",
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      "Iter #3047424:  Learning rate = 0.001469:   Batch Loss = 0.948629, Accuracy = 0.701416015625\n",
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      "Iter #3080192:  Learning rate = 0.001469:   Batch Loss = 0.942093, Accuracy = 0.705810546875\n",
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      "Iter #3112960:  Learning rate = 0.001411:   Batch Loss = 0.925079, Accuracy = 0.710205078125\n",
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      "Iter #3145728:  Learning rate = 0.001411:   Batch Loss = 0.922158, Accuracy = 0.71728515625\n",
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      "Iter #3178496:  Learning rate = 0.001411:   Batch Loss = 0.924179, Accuracy = 0.7158203125\n",
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      "Iter #3211264:  Learning rate = 0.001354:   Batch Loss = 0.947574, Accuracy = 0.696044921875\n",
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      "Iter #3244032:  Learning rate = 0.001354:   Batch Loss = 0.925359, Accuracy = 0.718017578125\n",
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      "Iter #3276800:  Learning rate = 0.001354:   Batch Loss = 0.956425, Accuracy = 0.704833984375\n",
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      "Iter #3309568:  Learning rate = 0.001300:   Batch Loss = 0.930149, Accuracy = 0.71337890625\n",
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      "Iter #3342336:  Learning rate = 0.001300:   Batch Loss = 0.920946, Accuracy = 0.72705078125\n",
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      "Iter #3375104:  Learning rate = 0.001300:   Batch Loss = 0.911280, Accuracy = 0.72216796875\n",
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      "Iter #3407872:  Learning rate = 0.001248:   Batch Loss = 0.924877, Accuracy = 0.708984375\n",
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      "Iter #3440640:  Learning rate = 0.001248:   Batch Loss = 0.907183, Accuracy = 0.724609375\n",
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      "Iter #3473408:  Learning rate = 0.001248:   Batch Loss = 0.917028, Accuracy = 0.716552734375\n",
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      "Iter #3506176:  Learning rate = 0.001198:   Batch Loss = 0.920178, Accuracy = 0.718994140625\n",
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      "Iter #3538944:  Learning rate = 0.001198:   Batch Loss = 0.915667, Accuracy = 0.720947265625\n",
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      "Iter #3571712:  Learning rate = 0.001198:   Batch Loss = 0.919695, Accuracy = 0.713134765625\n",
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      "Iter #3604480:  Learning rate = 0.001150:   Batch Loss = 0.914486, Accuracy = 0.72216796875\n",
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      "Iter #3637248:  Learning rate = 0.001150:   Batch Loss = 0.929343, Accuracy = 0.714599609375\n",
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      "Iter #3702784:  Learning rate = 0.001104:   Batch Loss = 0.926027, Accuracy = 0.716552734375\n",
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      "Iter #3735552:  Learning rate = 0.001104:   Batch Loss = 0.897160, Accuracy = 0.72900390625\n",
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      "Iter #3768320:  Learning rate = 0.001104:   Batch Loss = 0.907660, Accuracy = 0.722412109375\n",
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      "Iter #3801088:  Learning rate = 0.001060:   Batch Loss = 0.898653, Accuracy = 0.731689453125\n",
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      "Iter #3833856:  Learning rate = 0.001060:   Batch Loss = 0.906215, Accuracy = 0.720458984375\n",
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      "Iter #3866624:  Learning rate = 0.001060:   Batch Loss = 0.909038, Accuracy = 0.71728515625\n",
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      "Iter #3899392:  Learning rate = 0.001060:   Batch Loss = 0.897840, Accuracy = 0.72998046875\n",
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      "Iter #3932160:  Learning rate = 0.001018:   Batch Loss = 0.901386, Accuracy = 0.722412109375\n",
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      "Iter #3964928:  Learning rate = 0.001018:   Batch Loss = 0.900469, Accuracy = 0.726806640625\n",
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      "Iter #3997696:  Learning rate = 0.001018:   Batch Loss = 0.902288, Accuracy = 0.722900390625\n",
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      "Iter #4030464:  Learning rate = 0.000977:   Batch Loss = 0.899137, Accuracy = 0.73486328125\n",
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      "Iter #4063232:  Learning rate = 0.000977:   Batch Loss = 0.898519, Accuracy = 0.731201171875\n",
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      "Iter #4096000:  Learning rate = 0.000977:   Batch Loss = 0.918800, Accuracy = 0.708984375\n",
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      "Iter #4128768:  Learning rate = 0.000938:   Batch Loss = 0.895386, Accuracy = 0.729248046875\n",
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      "Iter #4161536:  Learning rate = 0.000938:   Batch Loss = 0.888623, Accuracy = 0.73193359375\n",
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      "Iter #4194304:  Learning rate = 0.000938:   Batch Loss = 0.898871, Accuracy = 0.7265625\n",
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      "Iter #4227072:  Learning rate = 0.000900:   Batch Loss = 0.898260, Accuracy = 0.730224609375\n",
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      "Iter #4259840:  Learning rate = 0.000900:   Batch Loss = 0.889022, Accuracy = 0.729736328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.915080428123, Accuracy = 0.735698163509\n",
      "Iter #4292608:  Learning rate = 0.000900:   Batch Loss = 0.890433, Accuracy = 0.74267578125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.908611118793, Accuracy = 0.746304988861\n",
      "Iter #4325376:  Learning rate = 0.000864:   Batch Loss = 0.889789, Accuracy = 0.7294921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.913319826126, Accuracy = 0.740566849709\n",
      "Iter #4358144:  Learning rate = 0.000864:   Batch Loss = 0.879834, Accuracy = 0.736572265625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.916429281235, Accuracy = 0.73761087656\n",
      "Iter #4390912:  Learning rate = 0.000864:   Batch Loss = 0.878883, Accuracy = 0.733154296875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.910310447216, Accuracy = 0.735176503658\n",
      "Iter #4423680:  Learning rate = 0.000830:   Batch Loss = 0.882228, Accuracy = 0.7294921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.907976150513, Accuracy = 0.74108850956\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #4456448:  Learning rate = 0.000830:   Batch Loss = 0.892080, Accuracy = 0.726806640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.911484479904, Accuracy = 0.742653429508\n",
      "Iter #4489216:  Learning rate = 0.000830:   Batch Loss = 0.875857, Accuracy = 0.73486328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.909143149853, Accuracy = 0.74039298296\n",
      "Iter #4521984:  Learning rate = 0.000796:   Batch Loss = 0.899562, Accuracy = 0.720947265625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.915482521057, Accuracy = 0.739523589611\n",
      "Iter #4554752:  Learning rate = 0.000796:   Batch Loss = 0.886076, Accuracy = 0.7353515625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.910280704498, Accuracy = 0.735698163509\n",
      "Iter #4587520:  Learning rate = 0.000796:   Batch Loss = 0.901717, Accuracy = 0.735595703125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.910605430603, Accuracy = 0.743348956108\n",
      "Iter #4620288:  Learning rate = 0.000765:   Batch Loss = 0.857303, Accuracy = 0.75146484375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.905155658722, Accuracy = 0.742653429508\n",
      "Iter #4653056:  Learning rate = 0.000765:   Batch Loss = 0.905488, Accuracy = 0.71728515625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.91036260128, Accuracy = 0.739523589611\n",
      "Iter #4685824:  Learning rate = 0.000765:   Batch Loss = 0.886646, Accuracy = 0.732177734375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.906064391136, Accuracy = 0.74439227581\n",
      "Iter #4718592:  Learning rate = 0.000734:   Batch Loss = 0.861132, Accuracy = 0.744384765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.901207089424, Accuracy = 0.746826648712\n",
      "Iter #4751360:  Learning rate = 0.000734:   Batch Loss = 0.885125, Accuracy = 0.73583984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.909750699997, Accuracy = 0.739871323109\n",
      "Iter #4784128:  Learning rate = 0.000734:   Batch Loss = 0.884789, Accuracy = 0.733154296875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.908295094967, Accuracy = 0.741610169411\n",
      "Iter #4816896:  Learning rate = 0.000705:   Batch Loss = 0.887271, Accuracy = 0.72998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.904837608337, Accuracy = 0.745261669159\n",
      "Iter #4849664:  Learning rate = 0.000705:   Batch Loss = 0.883896, Accuracy = 0.728759765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.901014924049, Accuracy = 0.748043835163\n",
      "Iter #4882432:  Learning rate = 0.000705:   Batch Loss = 0.863343, Accuracy = 0.746337890625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.910419285297, Accuracy = 0.741610169411\n",
      "Iter #4915200:  Learning rate = 0.000676:   Batch Loss = 0.869288, Accuracy = 0.74365234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.910153031349, Accuracy = 0.737089216709\n",
      "Iter #4947968:  Learning rate = 0.000676:   Batch Loss = 0.882924, Accuracy = 0.7412109375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.901822209358, Accuracy = 0.74508780241\n",
      "Iter #4980736:  Learning rate = 0.000676:   Batch Loss = 0.885442, Accuracy = 0.722412109375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.904002845287, Accuracy = 0.748739361763\n",
      "Iter #5013504:  Learning rate = 0.000649:   Batch Loss = 0.866377, Accuracy = 0.748046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.903672814369, Accuracy = 0.748565495014\n",
      "Iter #5046272:  Learning rate = 0.000649:   Batch Loss = 0.875009, Accuracy = 0.7314453125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.9012824893, Accuracy = 0.744566142559\n",
      "Iter #5079040:  Learning rate = 0.000649:   Batch Loss = 0.872920, Accuracy = 0.736572265625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.908038496971, Accuracy = 0.740045189857\n",
      "Iter #5111808:  Learning rate = 0.000623:   Batch Loss = 0.884204, Accuracy = 0.724365234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.904364347458, Accuracy = 0.745261669159\n",
      "Iter #5144576:  Learning rate = 0.000623:   Batch Loss = 0.868219, Accuracy = 0.7421875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.901244580746, Accuracy = 0.749087095261\n",
      "Iter #5177344:  Learning rate = 0.000623:   Batch Loss = 0.870599, Accuracy = 0.73779296875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.904945313931, Accuracy = 0.748217701912\n",
      "Iter #5210112:  Learning rate = 0.000599:   Batch Loss = 0.880660, Accuracy = 0.73486328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.900817871094, Accuracy = 0.748739361763\n",
      "Iter #5242880:  Learning rate = 0.000599:   Batch Loss = 0.878037, Accuracy = 0.733642578125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.899732470512, Accuracy = 0.74786990881\n",
      "Iter #5275648:  Learning rate = 0.000599:   Batch Loss = 0.887296, Accuracy = 0.729248046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.899254322052, Accuracy = 0.750130414963\n",
      "Iter #5308416:  Learning rate = 0.000575:   Batch Loss = 0.858360, Accuracy = 0.742919921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.905005931854, Accuracy = 0.745261669159\n",
      "Iter #5341184:  Learning rate = 0.000575:   Batch Loss = 0.865061, Accuracy = 0.747314453125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.902087330818, Accuracy = 0.742653429508\n",
      "Iter #5373952:  Learning rate = 0.000575:   Batch Loss = 0.870765, Accuracy = 0.73779296875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.901174664497, Accuracy = 0.750825941563\n",
      "Iter #5406720:  Learning rate = 0.000552:   Batch Loss = 0.851431, Accuracy = 0.7490234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.896157503128, Accuracy = 0.750652074814\n",
      "Iter #5439488:  Learning rate = 0.000552:   Batch Loss = 0.857144, Accuracy = 0.7451171875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.899524569511, Accuracy = 0.751173734665\n",
      "Iter #5472256:  Learning rate = 0.000552:   Batch Loss = 0.866858, Accuracy = 0.742431640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.898025095463, Accuracy = 0.752390861511\n",
      "Iter #5505024:  Learning rate = 0.000530:   Batch Loss = 0.864119, Accuracy = 0.736328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.902137041092, Accuracy = 0.742479562759\n",
      "Iter #5537792:  Learning rate = 0.000530:   Batch Loss = 0.866720, Accuracy = 0.738037109375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.897939682007, Accuracy = 0.750825941563\n",
      "Iter #5570560:  Learning rate = 0.000530:   Batch Loss = 0.852146, Accuracy = 0.7470703125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.894379854202, Accuracy = 0.750825941563\n",
      "Iter #5603328:  Learning rate = 0.000508:   Batch Loss = 0.869888, Accuracy = 0.740234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.897728204727, Accuracy = 0.749087095261\n",
      "Iter #5636096:  Learning rate = 0.000508:   Batch Loss = 0.873304, Accuracy = 0.74609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.903082609177, Accuracy = 0.748043835163\n",
      "Iter #5668864:  Learning rate = 0.000508:   Batch Loss = 0.870472, Accuracy = 0.74169921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.900198101997, Accuracy = 0.748913228512\n",
      "Iter #5701632:  Learning rate = 0.000488:   Batch Loss = 0.867663, Accuracy = 0.739013671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.90093255043, Accuracy = 0.747522175312\n",
      "Iter #5734400:  Learning rate = 0.000488:   Batch Loss = 0.852086, Accuracy = 0.748291015625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.899989187717, Accuracy = 0.748565495014\n",
      "Iter #5767168:  Learning rate = 0.000488:   Batch Loss = 0.885048, Accuracy = 0.731201171875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.903499662876, Accuracy = 0.74578332901\n",
      "Iter #5799936:  Learning rate = 0.000488:   Batch Loss = 0.864411, Accuracy = 0.741455078125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.903959393501, Accuracy = 0.742479562759\n",
      "Iter #5832704:  Learning rate = 0.000468:   Batch Loss = 0.866556, Accuracy = 0.73828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.90045106411, Accuracy = 0.749608755112\n",
      "Iter #5865472:  Learning rate = 0.000468:   Batch Loss = 0.858560, Accuracy = 0.744873046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.897506058216, Accuracy = 0.750304281712\n",
      "Iter #5898240:  Learning rate = 0.000468:   Batch Loss = 0.871844, Accuracy = 0.734375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.902315735817, Accuracy = 0.74369674921\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #5931008:  Learning rate = 0.000450:   Batch Loss = 0.863873, Accuracy = 0.744140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.897553145885, Accuracy = 0.749434888363\n",
      "Iter #5963776:  Learning rate = 0.000450:   Batch Loss = 0.875134, Accuracy = 0.750732421875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.899381875992, Accuracy = 0.744913935661\n",
      "Iter #5996544:  Learning rate = 0.000450:   Batch Loss = 0.867399, Accuracy = 0.737548828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.900442004204, Accuracy = 0.746131122112\n",
      "Iter #6029312:  Learning rate = 0.000432:   Batch Loss = 0.850885, Accuracy = 0.748046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.897583425045, Accuracy = 0.750130414963\n",
      "Iter #6062080:  Learning rate = 0.000432:   Batch Loss = 0.861727, Accuracy = 0.743896484375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.899170875549, Accuracy = 0.748565495014\n",
      "Iter #6094848:  Learning rate = 0.000432:   Batch Loss = 0.860248, Accuracy = 0.742431640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.900868058205, Accuracy = 0.747348308563\n",
      "Iter #6127616:  Learning rate = 0.000414:   Batch Loss = 0.847575, Accuracy = 0.750732421875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.899083852768, Accuracy = 0.743870615959\n",
      "Iter #6160384:  Learning rate = 0.000414:   Batch Loss = 0.891082, Accuracy = 0.731201171875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.89814555645, Accuracy = 0.744740068913\n",
      "Iter #6193152:  Learning rate = 0.000414:   Batch Loss = 0.855929, Accuracy = 0.741455078125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.89822524786, Accuracy = 0.745957195759\n",
      "Iter #6225920:  Learning rate = 0.000398:   Batch Loss = 0.855141, Accuracy = 0.748779296875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.893675923347, Accuracy = 0.745609462261\n",
      "Iter #6258688:  Learning rate = 0.000398:   Batch Loss = 0.841883, Accuracy = 0.748291015625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.894460499287, Accuracy = 0.751347601414\n",
      "Iter #6291456:  Learning rate = 0.000398:   Batch Loss = 0.870395, Accuracy = 0.7412109375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.891210794449, Accuracy = 0.750999808311\n",
      "Iter #6324224:  Learning rate = 0.000382:   Batch Loss = 0.855835, Accuracy = 0.74755859375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.898136615753, Accuracy = 0.751173734665\n",
      "Iter #6356992:  Learning rate = 0.000382:   Batch Loss = 0.863945, Accuracy = 0.74609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.888926148415, Accuracy = 0.752390861511\n",
      "Iter #6389760:  Learning rate = 0.000382:   Batch Loss = 0.854050, Accuracy = 0.745849609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.893393278122, Accuracy = 0.752564787865\n",
      "Iter #6422528:  Learning rate = 0.000367:   Batch Loss = 0.840983, Accuracy = 0.76025390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.891884922981, Accuracy = 0.752043128014\n",
      "Iter #6455296:  Learning rate = 0.000367:   Batch Loss = 0.851819, Accuracy = 0.74755859375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.893710017204, Accuracy = 0.752564787865\n",
      "Iter #6488064:  Learning rate = 0.000367:   Batch Loss = 0.861734, Accuracy = 0.744873046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.892189383507, Accuracy = 0.751521468163\n",
      "Iter #6520832:  Learning rate = 0.000352:   Batch Loss = 0.842245, Accuracy = 0.75634765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.896037817001, Accuracy = 0.750130414963\n",
      "Iter #6553600:  Learning rate = 0.000352:   Batch Loss = 0.863147, Accuracy = 0.741943359375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.890854775906, Accuracy = 0.753608047962\n",
      "Iter #6586368:  Learning rate = 0.000352:   Batch Loss = 0.868099, Accuracy = 0.739013671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.891100049019, Accuracy = 0.751173734665\n",
      "Iter #6619136:  Learning rate = 0.000338:   Batch Loss = 0.852842, Accuracy = 0.744873046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.891520619392, Accuracy = 0.750130414963\n",
      "Iter #6651904:  Learning rate = 0.000338:   Batch Loss = 0.864790, Accuracy = 0.740966796875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.898521065712, Accuracy = 0.74786990881\n",
      "Iter #6684672:  Learning rate = 0.000338:   Batch Loss = 0.848709, Accuracy = 0.747802734375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.889864742756, Accuracy = 0.750652074814\n",
      "Iter #6717440:  Learning rate = 0.000324:   Batch Loss = 0.872107, Accuracy = 0.744140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.888407230377, Accuracy = 0.754129707813\n",
      "Iter #6750208:  Learning rate = 0.000324:   Batch Loss = 0.856538, Accuracy = 0.74658203125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.884963750839, Accuracy = 0.754651367664\n",
      "Iter #6782976:  Learning rate = 0.000324:   Batch Loss = 0.857981, Accuracy = 0.747314453125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.886199474335, Accuracy = 0.751521468163\n",
      "Optimization Finished!\n",
      "FINAL RESULT: Batch Loss = 0.885648131371, Accuracy = 0.752043128014\n",
      "TOTAL TIME:  789.372411013\n"
     ]
    }
   ],
   "source": [
    "test_losses = []\n",
    "test_accuracies = []\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "# Perform Training steps with \"batch_size\" amount of data at each loop. \n",
    "# Elements of each batch are chosen randomly, without replacement, from X_train, \n",
    "# restarting when remaining datapoints < batch_size\n",
    "step = 1\n",
    "time_start = time.time()\n",
    "unsampled_indices = range(0,len(X_train))\n",
    "\n",
    "while step * batch_size <= training_iters:\n",
    "    #print (sess.run(learning_rate)) #decaying learning rate\n",
    "    #print (sess.run(global_step)) # global number of iterations\n",
    "    if len(unsampled_indices) < batch_size:\n",
    "        unsampled_indices = range(0,len(X_train)) \n",
    "    batch_xs, raw_labels, unsampled_indicies = extract_batch_size(X_train, y_train, unsampled_indices, batch_size)\n",
    "    batch_ys = one_hot(raw_labels)\n",
    "    # check that encoded output is same length as num_classes, if not, pad it \n",
    "    if len(batch_ys[0]) < n_classes:\n",
    "        temp_ys = np.zeros((batch_size, n_classes))\n",
    "        temp_ys[:batch_ys.shape[0],:batch_ys.shape[1]] = batch_ys\n",
    "        batch_ys = temp_ys\n",
    "       \n",
    "    \n",
    "\n",
    "    # Fit training using batch data\n",
    "    _, loss, acc = sess.run(\n",
    "        [optimizer, cost, accuracy],\n",
    "        feed_dict={\n",
    "            x: batch_xs, \n",
    "            y: batch_ys\n",
    "        }\n",
    "    )\n",
    "    train_losses.append(loss)\n",
    "    train_accuracies.append(acc)\n",
    "    \n",
    "    # Evaluate network only at some steps for faster training: \n",
    "    if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):\n",
    "        \n",
    "        # To not spam console, show training accuracy/loss in this \"if\"\n",
    "        print(\"Iter #\" + str(step*batch_size) + \\\n",
    "              \":  Learning rate = \" + \"{:.6f}\".format(sess.run(learning_rate)) + \\\n",
    "              \":   Batch Loss = \" + \"{:.6f}\".format(loss) + \\\n",
    "              \", Accuracy = {}\".format(acc))\n",
    "        \n",
    "        # Evaluation on the test set (no learning made here - just evaluation for diagnosis)\n",
    "        loss, acc = sess.run(\n",
    "            [cost, accuracy], \n",
    "            feed_dict={\n",
    "                x: X_test,\n",
    "                y: one_hot(y_test)\n",
    "            }\n",
    "        )\n",
    "        test_losses.append(loss)\n",
    "        test_accuracies.append(acc)\n",
    "        print(\"PERFORMANCE ON TEST SET:             \" + \\\n",
    "              \"Batch Loss = {}\".format(loss) + \\\n",
    "              \", Accuracy = {}\".format(acc))\n",
    "\n",
    "    step += 1\n",
    "\n",
    "print(\"Optimization Finished!\")\n",
    "\n",
    "# Accuracy for test data\n",
    "\n",
    "one_hot_predictions, accuracy, final_loss = sess.run(\n",
    "    [pred, accuracy, cost],\n",
    "    feed_dict={\n",
    "        x: X_test,\n",
    "        y: one_hot(y_test)\n",
    "    }\n",
    ")\n",
    "\n",
    "test_losses.append(final_loss)\n",
    "test_accuracies.append(accuracy)\n",
    "\n",
    "print(\"FINAL RESULT: \" + \\\n",
    "      \"Batch Loss = {}\".format(final_loss) + \\\n",
    "      \", Accuracy = {}\".format(accuracy))\n",
    "time_stop = time.time()\n",
    "print(\"TOTAL TIME:  {}\".format(time_stop - time_start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "209\n",
      "1657\n"
     ]
    },
    {
     "data": {
      "image/png": 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pzT4f27NOT77p8k2O/bG2WAzDYM6+OWw8tdG9PzIikvZV2gMwcv1IPln3CaAH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\nTRvatm1LYmI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93++eBoVbXKpD7L3KTcO5aRSo7EJgarjFpjrEH+cBPmX93ruP0yic4hLK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\niIhIhf1/jKTnco84n9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa723069750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy: 75.2043128014%\n",
      "\n",
      "Precision: 73.1647299623%\n",
      "Recall: 75.2043122935%\n",
      "f1_score: 73.5824284686%\n",
      "\n",
      "Confusion Matrix:\n",
      "Created using test set of 5751 datapoints, normalised to % of each class in the test dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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raUk5bpfV9yzqpWx+/AR1/ZIWONyky6a4hiT3ZGHD+nrp6Uf9wHcq7cLz4BHr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YGNMEk3lnzJ48BNwC+BYyPCVdJ6kDQvcAHwGuas4NXub8DGEfFMjzbWhaRNSIVSdqEwpMpDUXvz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6d21fced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (Inline plots: )\n",
    "%matplotlib inline\n",
    "\n",
    "font = {\n",
    "    'family' : 'Bitstream Vera Sans',\n",
    "    'weight' : 'bold',\n",
    "    'size'   : 18\n",
    "}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "width = 12\n",
    "height = 12\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))\n",
    "#plt.plot(indep_train_axis, np.array(train_losses),     \"b--\", label=\"Train losses\")\n",
    "plt.plot(indep_train_axis, np.array(train_accuracies), \"g--\", label=\"Train accuracies\")\n",
    "\n",
    "indep_test_axis = np.append(\n",
    "    np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1]),\n",
    "    [training_iters]\n",
    ")\n",
    "#plt.plot(indep_test_axis, np.array(test_losses), \"b-\", linewidth=2.0, label=\"Test losses\")\n",
    "plt.plot(indep_test_axis, np.array(test_accuracies), \"b-\", linewidth=2.0, label=\"Test accuracies\")\n",
    "print len(test_accuracies)\n",
    "print len(train_accuracies)\n",
    "\n",
    "plt.title(\"Training session's Accuracy over Iterations\")\n",
    "plt.legend(loc='lower right', shadow=True)\n",
    "plt.ylabel('Training Accuracy')\n",
    "plt.xlabel('Training Iteration')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Results\n",
    "\n",
    "predictions = one_hot_predictions.argmax(1)\n",
    "\n",
    "print(\"Testing Accuracy: {}%\".format(100*accuracy))\n",
    "\n",
    "print(\"\")\n",
    "print(\"Precision: {}%\".format(100*metrics.precision_score(y_test, predictions, average=\"weighted\")))\n",
    "print(\"Recall: {}%\".format(100*metrics.recall_score(y_test, predictions, average=\"weighted\")))\n",
    "print(\"f1_score: {}%\".format(100*metrics.f1_score(y_test, predictions, average=\"weighted\")))\n",
    "\n",
    "print(\"\")\n",
    "print(\"Confusion Matrix:\")\n",
    "print(\"Created using test set of {} datapoints, normalised to % of each class in the test dataset\".format(len(y_test)))\n",
    "confusion_matrix = metrics.confusion_matrix(y_test, predictions)\n",
    "\n",
    "\n",
    "#print(confusion_matrix)\n",
    "normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100\n",
    "\n",
    "\n",
    "# Plot Results: \n",
    "width = 12\n",
    "height = 12\n",
    "plt.figure(figsize=(width, height))\n",
    "plt.imshow(\n",
    "    normalised_confusion_matrix, \n",
    "    interpolation='nearest', \n",
    "    cmap=plt.cm.Blues\n",
    ")\n",
    "plt.title(\"Confusion matrix \\n(normalised to % of total test data)\")\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(n_classes)\n",
    "plt.xticks(tick_marks, LABELS, rotation=90)\n",
    "plt.yticks(tick_marks, LABELS)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#X_val_path = DATASET_PATH + \"X_val.txt\"\n",
    "#X_val = load_X(X_val_path)\n",
    "#print X_val\n",
    "#\n",
    "#preds = sess.run(\n",
    "#    [pred],\n",
    "#    feed_dict={\n",
    "#        x: X_val\n",
    "#   }\n",
    "#)\n",
    "#\n",
    "#print preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22830813, 0.22900365, 0.23282908, 0.29386193, 0.28047296, 0.30412102, 0.28603721, 0.30864197, 0.29368806, 0.33733264, 0.35489479, 0.38723701, 0.39001912, 0.41245002, 0.39871326, 0.40166926, 0.44044513, 0.43835855, 0.43088159, 0.42462179, 0.37715179, 0.43244654, 0.43714136, 0.44705269, 0.44618326, 0.44253173, 0.46044165, 0.44392279, 0.43122935, 0.44705269, 0.43348983, 0.45122588, 0.41523212, 0.41540602, 0.43122935, 0.42288297, 0.43192488, 0.47070074, 0.46965745, 0.49156669, 0.52269173, 0.52495217, 0.52512604, 0.53538513, 0.54112327, 0.54164493, 0.54147106, 0.54599202, 0.55868542, 0.55364287, 0.56059813, 0.55572945, 0.56737959, 0.56129366, 0.55659884, 0.56720573, 0.5633803, 0.58146411, 0.58372456, 0.59328812, 0.59346199, 0.59607023, 0.55468613, 0.56094593, 0.61902279, 0.62893409, 0.63658494, 0.66214573, 0.6685794, 0.6784907, 0.67310035, 0.6906625, 0.70126933, 0.71031123, 0.69935662, 0.68614155, 0.69587898, 0.68648928, 0.694314, 0.69796556, 0.69918275, 0.71657103, 0.72422189, 0.71152842, 0.72630847, 0.72456962, 0.71657103, 0.70248652, 0.72317857, 0.72665626, 0.73100328, 0.71796209, 0.72856897, 0.72456962, 0.72283083, 0.73308992, 0.72248304, 0.71048516, 0.71709269, 0.73482871, 0.73395932, 0.72283083, 0.73274213, 0.73065555, 0.73030776, 0.73656756, 0.73500264, 0.71952707, 0.73621976, 0.73656756, 0.73482871, 0.72387409, 0.738828, 0.73813248, 0.73535037, 0.74021912, 0.73900193, 0.73795861, 0.73726308, 0.72909057, 0.73482871, 0.74091464, 0.73656756, 0.74074072, 0.732916, 0.73535037, 0.7419579, 0.73656756, 0.74213183, 0.74804384, 0.73569816, 0.74630499, 0.74056685, 0.73761088, 0.7351765, 0.74108851, 0.74265343, 0.74039298, 0.73952359, 0.73569816, 0.74334896, 0.74265343, 0.73952359, 0.74439228, 0.74682665, 0.73987132, 0.74161017, 0.74526167, 0.74804384, 0.74161017, 0.73708922, 0.7450878, 0.74873936, 0.7485655, 0.74456614, 0.74004519, 0.74526167, 0.7490871, 0.7482177, 0.74873936, 0.74786991, 0.75013041, 0.74526167, 0.74265343, 0.75082594, 0.75065207, 0.75117373, 0.75239086, 0.74247956, 0.75082594, 0.75082594, 0.7490871, 0.74804384, 0.74891323, 0.74752218, 0.7485655, 0.74578333, 0.74247956, 0.74960876, 0.75030428, 0.74369675, 0.74943489, 0.74491394, 0.74613112, 0.75013041, 0.7485655, 0.74734831, 0.74387062, 0.74474007, 0.7459572, 0.74560946, 0.7513476, 0.75099981, 0.75117373, 0.75239086, 0.75256479, 0.75204313, 0.75256479, 0.75152147, 0.75013041, 0.75360805, 0.75117373, 0.75013041, 0.74786991, 0.75065207, 0.75412971, 0.75465137, 0.75152147, 0.75204313]\n"
     ]
    }
   ],
   "source": [
    "#sess.close()\n",
    "print test_accuracies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Final accuracy of >90% is pretty good, considering that training takes about 7 minutes.\n",
    "\n",
    "Noticeable confusion between activities of Clapping Hands and Boxing, and between Jumping Jacks and Waving Two Hands which is understandable.\n",
    "\n",
    "In terms of the applicability of this to a wider dataset, I would imagine that it would be able to work for any activities in which the training included a views from all angles to be tested on. It would be interesting to see it's applicability to camera angles in between the 4 used in this dataset, without training on them specifically.\n",
    "\n",
    " Overall, this experiment validates the idea that 2D pose can be used for at least human activity recognition, and provides verification to continue onto use of 2D pose for behaviour estimation in both people and animals\n",
    " \n",
    "\n",
    " ### With regards to Using LSTM-RNNs\n",
    " - Batch sampling\n",
    "     - It is neccessary to ensure you are not just sampling classes one at a time! (ie y_train is ordered by class and batch chosen in order)The use of random sampling of batches without replacement from the training data resolves this.    \n",
    " \n",
    " - Architecture\n",
    "     - Testing has been run using a variety of hidden units per LSTM cell, with results showing that testing accuracy achieves a higher score when using a number of hidden cells approximately equal to that of the input, ie 34. The following figure displays the final accuracy achieved on the testing dataset for a variety of hidden units, all using a batch size of 4096 and 300 epochs (a total of 1657 iterations, with testing performed every 8th iteration).\n",
    "   \n",
    " \n",
    " \n",
    "\n",
    "## Future Works\n",
    "\n",
    "Inclusion of :\n",
    "\n",
    " - A pipeline for qualitative results\n",
    " - A validation dataset\n",
    " - Momentum     \n",
    " - Normalise input data (each point with respect to distribution of itself only)\n",
    " - Dropout\n",
    " - Comparison of effect of changing batch size\n",
    " \n",
    "\n",
    "Further research will be made into the use on more subtle activity classes, such as walking versus running, agitated movement versus calm movement, and perhaps normal versus abnormal behaviour, based on a baseline of normal motion.\n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "The dataset can be found at http://tele-immersion.citris-uc.org/berkeley_mhad released under the BSD-2 license\n",
    ">Copyright (c) 2013, Regents of the University of California All rights reserved.\n",
    "\n",
    "The network used in this experiment is based on the following, available under the [MIT License](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/blob/master/LICENSE). :\n",
    "> Guillaume Chevalier, LSTMs for Human Activity Recognition, 2016\n",
    "> https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's convert this notebook to a README for the GitHub project's title page:\n",
    "!jupyter nbconvert --to markdown LSTM.ipynb\n",
    "!mv LSTM.md README.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  }
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